import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import (
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
         MraModel,
     )
     from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						List[str]=2   ,   lowerCamelCase_						:						int=8   ,   lowerCamelCase_						:						int=True   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Optional[int]=True   ,   lowerCamelCase_						:						List[Any]=99   ,   lowerCamelCase_						:						Dict=16   ,   lowerCamelCase_						:						int=5   ,   lowerCamelCase_						:						Any=2   ,   lowerCamelCase_						:						Optional[int]=36   ,   lowerCamelCase_						:						List[Any]="gelu"   ,   lowerCamelCase_						:						int=0.0   ,   lowerCamelCase_						:						str=0.0   ,   lowerCamelCase_						:						List[str]=512   ,   lowerCamelCase_						:						int=16   ,   lowerCamelCase_						:						Any=2   ,   lowerCamelCase_						:						Any=0.0_2   ,   lowerCamelCase_						:						int=3   ,   lowerCamelCase_						:						int=4   ,   lowerCamelCase_						:						Optional[int]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      parent
    UpperCamelCase			  =      batch_size
    UpperCamelCase			  =      seq_length
    UpperCamelCase			  =      is_training
    UpperCamelCase			  =      use_input_mask
    UpperCamelCase			  =      use_token_type_ids
    UpperCamelCase			  =      use_labels
    UpperCamelCase			  =      vocab_size
    UpperCamelCase			  =      hidden_size
    UpperCamelCase			  =      num_hidden_layers
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      intermediate_size
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      hidden_dropout_prob
    UpperCamelCase			  =      attention_probs_dropout_prob
    UpperCamelCase			  =      max_position_embeddings
    UpperCamelCase			  =      type_vocab_size
    UpperCamelCase			  =      type_sequence_label_size
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      num_labels
    UpperCamelCase			  =      num_choices
    UpperCamelCase			  =      scope







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size				)

    UpperCamelCase			  =      None
    if self.use_input_mask:
     UpperCamelCase			  =      random_attention_mask([self.batch_size, self.seq_length]				)

    UpperCamelCase			  =      None
    if self.use_token_type_ids:
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size				)

    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    UpperCamelCase			  =      None
    if self.use_labels:
     UpperCamelCase			  =      ids_tensor([self.batch_size]   ,   self.type_sequence_label_size				)
     UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels				)
     UpperCamelCase			  =      ids_tensor([self.batch_size]   ,   self.num_choices				)

    UpperCamelCase			  =      self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return MraConfig(
        vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   is_decoder=lowerCamelCase_   ,   initializer_range=self.initializer_range   ,   )







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.get_config()
    UpperCamelCase			  =      300
    return config







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    (
        (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     
    )			  =      self.prepare_config_and_inputs()

    UpperCamelCase			  =      True
    UpperCamelCase			  =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
    UpperCamelCase			  =      ids_tensor([self.batch_size, self.seq_length]   ,   vocab_size=2				)

    return (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        encoder_hidden_states,
        encoder_attention_mask,
    )







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MraModel(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_				)
    UpperCamelCase			  =      model(lowerCamelCase_   ,   token_type_ids=lowerCamelCase_				)
    UpperCamelCase			  =      model(lowerCamelCase_				)
    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      True
    UpperCamelCase			  =      MraModel(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   )
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_				)
    self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      MraForMaskedLM(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      MraForQuestionAnswering(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   start_positions=lowerCamelCase_   ,   end_positions=lowerCamelCase_   ,   )
    self.parent.assertEqual(result.start_logits.shape   ,   (self.batch_size, self.seq_length)				)
    self.parent.assertEqual(result.end_logits.shape   ,   (self.batch_size, self.seq_length)				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.num_labels
    UpperCamelCase			  =      MraForSequenceClassification(lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      self.num_labels
    UpperCamelCase			  =      MraForTokenClassification(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      model(lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   labels=lowerCamelCase_				)
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.num_choices
    UpperCamelCase			  =      MraForMultipleChoice(config=lowerCamelCase_				)
    model.to(lowerCamelCase_				)
    model.eval()
    UpperCamelCase			  =      input_ids.unsqueeze(1				).expand(-1   ,   self.num_choices   ,   -1				).contiguous()
    UpperCamelCase			  =      token_type_ids.unsqueeze(1				).expand(-1   ,   self.num_choices   ,   -1				).contiguous()
    UpperCamelCase			  =      input_mask.unsqueeze(1				).expand(-1   ,   self.num_choices   ,   -1				).contiguous()
    UpperCamelCase			  =      model(
        lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   token_type_ids=lowerCamelCase_   ,   labels=lowerCamelCase_   ,   )
    self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_choices)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self.prepare_config_and_inputs()
    (
        (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     (
        UpperCamelCase
    )  ,     
    )			  =      config_and_inputs
    UpperCamelCase			  =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
    return config, inputs_dict





@require_torch
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				(
       (
           MraModel,
           MraForMaskedLM,
           MraForMultipleChoice,
           MraForQuestionAnswering,
           MraForSequenceClassification,
           MraForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				False

   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      MraModelTester(self				)
    UpperCamelCase			  =      ConfigTester(self   ,   config_class=lowerCamelCase_   ,   hidden_size=37				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    self.config_tester.run_common_tests()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
     UpperCamelCase			  =      type
     self.model_tester.create_and_check_model(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_				)







   @slow
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCamelCase			  =      MraModel.from_pretrained(lowerCamelCase_				)
     self.assertIsNotNone(lowerCamelCase_				)







   @unittest.skip(reason="""MRA does not output attentions"""				)
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return





@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @slow
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      MraModel.from_pretrained("""uw-madison/mra-base-512-4"""				)
    UpperCamelCase			  =      torch.arange(256				).unsqueeze(0				)

    with torch.no_grad():
     UpperCamelCase			  =      model(lowerCamelCase_				)[0]

    UpperCamelCase			  =      torch.Size((1, 256, 768)				)
    self.assertEqual(output.shape   ,   lowerCamelCase_				)

    UpperCamelCase			  =      torch.tensor(
        [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]				)

    self.assertTrue(torch.allclose(output[:, :3, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""				)
    UpperCamelCase			  =      torch.arange(256				).unsqueeze(0				)

    with torch.no_grad():
     UpperCamelCase			  =      model(lowerCamelCase_				)[0]

    UpperCamelCase			  =      5_0265

    UpperCamelCase			  =      torch.Size((1, 256, vocab_size)				)
    self.assertEqual(output.shape   ,   lowerCamelCase_				)

    UpperCamelCase			  =      torch.tensor(
        [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]				)

    self.assertTrue(torch.allclose(output[:, :3, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)







   @slow
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""				)
    UpperCamelCase			  =      torch.arange(4096				).unsqueeze(0				)

    with torch.no_grad():
     UpperCamelCase			  =      model(lowerCamelCase_				)[0]

    UpperCamelCase			  =      5_0265

    UpperCamelCase			  =      torch.Size((1, 4096, vocab_size)				)
    self.assertEqual(output.shape   ,   lowerCamelCase_				)

    UpperCamelCase			  =      torch.tensor(
        [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]				)

    self.assertTrue(torch.allclose(output[:, :3, :3]   ,   lowerCamelCase_   ,   atol=1E-4				)				)

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int = 128   ,   lowerCamelCase_						:						int = 256   ,   lowerCamelCase_						:						float = 2_0_0_0.0   ,   lowerCamelCase_						:						int = 768   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 12   ,   lowerCamelCase_						:						int = 64   ,   lowerCamelCase_						:						int = 2048   ,   lowerCamelCase_						:						float = 0.1   ,   ):





    """simple docstring"""


    super().__init__()

    UpperCamelCase			  =      nn.Sequential(
        nn.Linear(lowerCamelCase_   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   nn.Linear(d_model * 4   ,   d_model * 4   ,   bias=lowerCamelCase_				)   ,   nn.SiLU()   ,   )

    UpperCamelCase			  =      nn.Embedding(lowerCamelCase_   ,   lowerCamelCase_				)
    UpperCamelCase			  =      False

    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)

    UpperCamelCase			  =      nn.ModuleList()
    for lyr_num in range(lowerCamelCase_				):
     # FiLM conditional T5 decoder
     UpperCamelCase			  =      DecoderLayer(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
     self.decoders.append(lowerCamelCase_				)

    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)

    UpperCamelCase			  =      nn.Dropout(p=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.mul(query_input.unsqueeze(-1				)   ,   key_input.unsqueeze(-2				)				)
    return mask.unsqueeze(-3				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCamelCase			  =      get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,   embedding_dim=self.config.d_model   ,   max_period=self.config.max_decoder_noise_time   ,   ).to(dtype=self.dtype				)

    UpperCamelCase			  =      self.conditioning_emb(lowerCamelCase_				).unsqueeze(1				)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCamelCase			  =      decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCamelCase			  =      torch.broadcast_to(
        torch.arange(lowerCamelCase_   ,   device=decoder_input_tokens.device				)   ,   (batch, seq_length)   ,   )

    UpperCamelCase			  =      self.position_encoding(lowerCamelCase_				)

    UpperCamelCase			  =      self.continuous_inputs_projection(lowerCamelCase_				)
    inputs += position_encodings
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    # decoder: No padding present.
    UpperCamelCase			  =      torch.ones(
        decoder_input_tokens.shape[:2]   ,   device=decoder_input_tokens.device   ,   dtype=inputs.dtype				)

    # Translate encoding masks to encoder-decoder masks.
    UpperCamelCase			  =      [(x, self.encoder_decoder_mask(lowerCamelCase_   ,   lowerCamelCase_				)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCamelCase			  =      torch.cat([x[0] for x in encodings_and_encdec_masks]   ,   dim=1				)
    UpperCamelCase			  =      torch.cat([x[1] for x in encodings_and_encdec_masks]   ,   dim=-1				)

    for lyr in self.decoders:
     UpperCamelCase			  =      lyr(
         lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   encoder_attention_mask=lowerCamelCase_   ,   )[0]

    UpperCamelCase			  =      self.decoder_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.post_dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.spec_out(lowerCamelCase_				)
    return spec_out





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[str]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Any=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)				)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=lowerCamelCase_   ,   d_kv=lowerCamelCase_   ,   num_heads=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_   ,   )				)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_   ,   layer_norm_epsilon=lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer[0](
        lowerCamelCase_   ,   conditioning_emb=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    if encoder_hidden_states is not None:
     UpperCamelCase			  =      torch.where(encoder_attention_mask > 0   ,   0   ,   -1E10				).to(
         encoder_hidden_states.dtype				)

     UpperCamelCase			  =      self.layer[1](
         lowerCamelCase_   ,   key_value_states=lowerCamelCase_   ,   attention_mask=lowerCamelCase_   ,   )

    # Apply Film Conditional Feed Forward layer
    UpperCamelCase			  =      self.layer[-1](lowerCamelCase_   ,   lowerCamelCase_				)

    return (hidden_states,)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)

    if conditioning_emb is not None:
     UpperCamelCase			  =      self.FiLMLayer(lowerCamelCase_   ,   lowerCamelCase_				)

    # Self-attention block
    UpperCamelCase			  =      self.attention(lowerCamelCase_				)

    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)

    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      Attention(query_dim=lowerCamelCase_   ,   heads=lowerCamelCase_   ,   dim_head=lowerCamelCase_   ,   out_bias=lowerCamelCase_   ,   scale_qk=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    UpperCamelCase			  =      self.attention(
        lowerCamelCase_   ,   encoder_hidden_states=lowerCamelCase_   ,   attention_mask=attention_mask.squeeze(1				)   ,   )
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return layer_output





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      TaDenseGatedActDense(d_model=lowerCamelCase_   ,   d_ff=lowerCamelCase_   ,   dropout_rate=lowerCamelCase_				)
    UpperCamelCase			  =      TaFiLMLayer(in_features=d_model * 4   ,   out_features=lowerCamelCase_				)
    UpperCamelCase			  =      TaLayerNorm(lowerCamelCase_   ,   eps=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.layer_norm(lowerCamelCase_				)
    if conditioning_emb is not None:
     UpperCamelCase			  =      self.film(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      self.DenseReluDense(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states + self.dropout(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_				)
    UpperCamelCase			  =      nn.Dropout(lowerCamelCase_				)
    UpperCamelCase			  =      NewGELUActivation()







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.act(self.wi_a(lowerCamelCase_				)				)
    UpperCamelCase			  =      self.wi_a(lowerCamelCase_				)
    UpperCamelCase			  =      hidden_gelu * hidden_linear
    UpperCamelCase			  =      self.dropout(lowerCamelCase_				)

    UpperCamelCase			  =      self.wo(lowerCamelCase_				)
    return hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[int]   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=1E-6				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Parameter(torch.ones(lowerCamelCase_				)				)
    UpperCamelCase			  =      eps







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      hidden_states.to(torch.floataa				).pow(2				).mean(-1   ,   keepdim=lowerCamelCase_				)
    UpperCamelCase			  =      hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCamelCase			  =      hidden_states.to(self.weight.dtype				)

    return self.weight * hidden_states





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.0_4_4_7_1_5 * torch.pow(lowerCamelCase_   ,   3.0				))				))





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      nn.Linear(lowerCamelCase_   ,   out_features * 2   ,   bias=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      self.scale_bias(lowerCamelCase_				)
    UpperCamelCase  ,     UpperCamelCase			  =      torch.chunk(lowerCamelCase_   ,   2   ,   -1				)
    UpperCamelCase			  =      x * (1 + scale) + shift
    return x
